


'''simple docstring'''




def   _a		(lowercase__			:   str							,   lowercase__			:   str						)			->			float:






     """simple docstring"""

     def get_matched_characters(lowercase__			:   str							,   lowercase__			:   str						) -> str:
          __snake_case           =						[]
          __snake_case           =						min(len(_stra						)							,   len(_stra						)						) // 2
          for i, l in enumerate(_stra						):
               __snake_case           =						int(max(0							,   i - limit						)						)
               __snake_case           =						int(min(i + limit + 1							,   len(_stra						)						)						)
               if l in _stra[left:right]:
                    matched.append(lowercase__						)
                    __snake_case           =						f'{_stra[0:_stra.index(lowercase__						)]} {_stra[_stra.index(lowercase__						) + 1:]}'

          return "".join(lowercase__						)

     # matching characters
     __snake_case           =						get_matched_characters(lowercase__							,   lowercase__						)
     __snake_case           =						get_matched_characters(lowercase__							,   lowercase__						)
     __snake_case           =						len(lowercase__						)

     # transposition
     __snake_case           =						(
         len([(ca, ca) for ca, ca in zip(lowercase__							,   lowercase__						) if ca != ca]						) // 2
     )

     if not match_count:
          __snake_case           =						0.0
     else:
          __snake_case           =						(
              1
              / 3
              * (
                  match_count / len(lowercase__						)
                  + match_count / len(lowercase__						)
                  + (match_count - transpositions) / match_count
              )
          )

     # common prefix up to 4 characters
     __snake_case           =						0
     for ca, ca in zip(stra[:4]							,   stra[:4]						):
          if ca == ca:
               prefix_len += 1
          else:
               break

     return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(jaro_winkler("hello", "world"))






import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
							lowerCamelCase_    :			int    =	argparse.ArgumentParser()

							parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

							parser.add_argument(
							    """--txt2img_unclip""",
							    default="""kakaobrain/karlo-v1-alpha""",
							    type=str,
							    required=False,
							    help="""The pretrained txt2img unclip.""",
							)

							lowerCamelCase_    :			Dict    =	parser.parse_args()

							lowerCamelCase_    :			str    =	UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

							lowerCamelCase_    :			int    =	CLIPImageProcessor()
							lowerCamelCase_    :			Any    =	CLIPVisionModelWithProjection.from_pretrained("""openai/clip-vit-large-patch14""")

							lowerCamelCase_    :			Union[str, Any]    =	UnCLIPImageVariationPipeline(
							    decoder=txtaimg.decoder,
							    text_encoder=txtaimg.text_encoder,
							    tokenizer=txtaimg.tokenizer,
							    text_proj=txtaimg.text_proj,
							    feature_extractor=feature_extractor,
							    image_encoder=image_encoder,
							    super_res_first=txtaimg.super_res_first,
							    super_res_last=txtaimg.super_res_last,
							    decoder_scheduler=txtaimg.decoder_scheduler,
							    super_res_scheduler=txtaimg.super_res_scheduler,
							)

							imgaimg.save_pretrained(args.dump_path)






